Loading required libraries

from gapminder import gapminder
import pandas as pd

import plotly.express as px
from plotly.offline import init_notebook_mode
init_notebook_mode(connected = True)

pd.set_option('display.float_format', '{:.2f}'.format)

Loading and Inspecting the data

gapminder.head()

	country	continent	year	lifeExp	pop	gdpPercap
0	Afghanistan	Asia	1952	28.80	8425333	779.45
1	Afghanistan	Asia	1957	30.33	9240934	820.85
2	Afghanistan	Asia	1962	32.00	10267083	853.10
3	Afghanistan	Asia	1967	34.02	11537966	836.20
4	Afghanistan	Asia	1972	36.09	13079460	739.98

gapminder.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1704 entries, 0 to 1703
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   country    1704 non-null   object 
 1   continent  1704 non-null   object 
 2   year       1704 non-null   int64  
 3   lifeExp    1704 non-null   float64
 4   pop        1704 non-null   int64  
 5   gdpPercap  1704 non-null   float64
dtypes: float64(2), int64(2), object(2)
memory usage: 80.0+ KB

gapminder.describe().T

	count	mean	std	min	25%	50%	75%	max
year	1704.00	1979.50	17.27	1952.00	1965.75	1979.50	1993.25	2007.00
lifeExp	1704.00	59.47	12.92	23.60	48.20	60.71	70.85	82.60
pop	1704.00	29601212.32	106157896.74	60011.00	2793664.00	7023595.50	19585221.75	1318683096.00
gdpPercap	1704.00	7215.33	9857.45	241.17	1202.06	3531.85	9325.46	113523.13

print(f"We have data from {gapminder['year'].min()} to {gapminder['year'].max()}.")

We have data from 1952 to 2007.

Exploring the data

We have life expectancy, population and GDP per capita data from 1952 to 2007. Let's filter the data with year 2007 and explore it.

gapminder_2007 = gapminder[gapminder["year"] == 2007]

fig = px.scatter(data_frame = gapminder_2007, x = "gdpPercap", y = "lifeExp", color="continent")
fig.update_layout(title = "2007 data: Life Expectancy vs GDP Per Capita", xaxis_title = "GDP Per Capita", 
                 yaxis_title = "Life Expectancy")
fig.show("notebook")

fig = px.scatter(data_frame = gapminder_2007, x = "gdpPercap", y = "lifeExp", color = "continent", size = "pop", 
                 hover_name=  "country")
fig.update_layout(title = "2007 data: Life Expectancy vs GDP Per Capita", xaxis_title = "GDP Per Capita", 
                 yaxis_title = "Life Expectancy")
fig.show("notebook")

fig = px.scatter(data_frame = gapminder_2007, x = "pop", y = "lifeExp", color = "continent", 
                 log_x = True, hover_name = "country")
fig.update_layout(title = "2007 data: Life Expectancy vs Population", xaxis_title = "Population", 
                 yaxis_title = "Life Expectancy")
fig.show("notebook")

Above graph shows that Oceania, Europe and Americas Countries has high life Expectancy and GDP Per Capita as compared to Asia and Africa countries.

Now, let's explore how life expectancy varies between continents. We will use the same filtered data of 2007 year.

mean_life_exp = gapminder_2007.groupby("continent", as_index = False)["lifeExp"].mean()

mean_life_exp

	continent	lifeExp
0	Africa	54.81
1	Americas	73.61
2	Asia	70.73
3	Europe	77.65
4	Oceania	80.72

fig = px.bar(data_frame = mean_life_exp, x = "continent", y = "lifeExp", color = "continent")
fig.update_layout(title = "2007 data: Mean Life Expectancy of continents", xaxis_title = "Continent", 
                 yaxis_title = "Mean Life Expectancy", xaxis = {'categoryorder': 'total descending'})
fig.show("notebook")

Oceania has the highest mean life expectancy followed by Europe, Americas and Asia. Africa has worst mean life expectancy of all.

# Countries with worst Life Expectancy in each continent

min_life_exp_ind = gapminder_2007.groupby("continent")["lifeExp"].agg("idxmin").to_list()
gapminder.iloc[min_life_exp_ind, :].reset_index(drop = True)

	country	continent	year	lifeExp	pop	gdpPercap
0	Swaziland	Africa	2007	39.61	1133066	4513.48
1	Haiti	Americas	2007	60.92	8502814	1201.64
2	Afghanistan	Asia	2007	43.83	31889923	974.58
3	Turkey	Europe	2007	71.78	71158647	8458.28
4	New Zealand	Oceania	2007	80.20	4115771	25185.01

# Countries with best Life Expectancy in each continent

max_life_exp_ind = gapminder_2007.groupby("continent")["lifeExp"].agg("idxmax").to_list()
gapminder.iloc[max_life_exp_ind, :].reset_index(drop = True)

	country	continent	year	lifeExp	pop	gdpPercap
0	Reunion	Africa	2007	76.44	798094	7670.12
1	Canada	Americas	2007	80.65	33390141	36319.24
2	Japan	Asia	2007	82.60	127467972	31656.07
3	Iceland	Europe	2007	81.76	301931	36180.79
4	Australia	Oceania	2007	81.23	20434176	34435.37

# Distrubution of Life Expectancy

fig = px.box(data_frame = gapminder_2007, x = "continent", y = "lifeExp", color = "continent")
fig.update_layout(title = "2007 data: Life Expectancy of continents", xaxis_title = "Continent", 
                 yaxis_title = "Life Expectancy")
fig.show("notebook")

Life Expectancy varies more in Africa than other continent. Median Life Expectancy is also lowest among all.

# Distrubution of GDP Per Capita

fig = px.box(data_frame = gapminder_2007, x = "continent", y = "gdpPercap", color = "continent")
fig.update_layout(title = "2007 data: GDP Per Capita of continents", xaxis_title = "Continent", 
                 yaxis_title = "GDP Per Capita")
fig.show("notebook")

GDP Per Capita varies more in Asia and European countries as compared to countries in other continents.

Let's look at life expectancy, population and GDP per capita data of India.

gapminder_india = gapminder[gapminder["country"] == "India"]
gapminder_india

	country	continent	year	lifeExp	pop	gdpPercap
696	India	Asia	1952	37.37	372000000	546.57
697	India	Asia	1957	40.25	409000000	590.06
698	India	Asia	1962	43.60	454000000	658.35
699	India	Asia	1967	47.19	506000000	700.77
700	India	Asia	1972	50.65	567000000	724.03
701	India	Asia	1977	54.21	634000000	813.34
702	India	Asia	1982	56.60	708000000	855.72
703	India	Asia	1987	58.55	788000000	976.51
704	India	Asia	1992	60.22	872000000	1164.41
705	India	Asia	1997	61.77	959000000	1458.82
706	India	Asia	2002	62.88	1034172547	1746.77
707	India	Asia	2007	64.70	1110396331	2452.21

fig = px.line(data_frame = gapminder_india, x = "year", y = "lifeExp",  markers = True)
fig.update_layout(title = "Life Expectancy of India", xaxis_title = "Year", 
                 yaxis_title = "Life Expectancy")
fig.show("notebook")

fig = px.line(data_frame = gapminder_india, x = "year", y = "pop", markers = True)
fig.update_layout(title = "Population of India", xaxis_title = "Year", 
                 yaxis_title = "Population")
fig.show("notebook")

fig = px.line(data_frame = gapminder_india, x = "year", y = "gdpPercap", markers = True)
fig.update_layout(title = "GDP Per Capita of India", xaxis_title = "Year", 
                 yaxis_title = "GDP Per Capita")
fig.show("notebook")

Life Expectancy of India has increased from 37.373 years to 64.698 years.

Population of India has increased dramtically from 372 Million to 1.11 Billion.

GDP Per Capita has increased from 546.56 to 2452.21.

corr = gapminder[["lifeExp", "pop", "gdpPercap"]].corr()
corr

	lifeExp	pop	gdpPercap
lifeExp	1.00	0.06	0.58
pop	0.06	1.00	-0.03
gdpPercap	0.58	-0.03	1.00

fig = px.imshow(corr, zmin = -1, zmax = 1, text_auto = ".2f")
fig.show("notebook")

GDP Per Capita and Life Expectancy has high positive correlation between them.

country_list = ["United States", "United Kingdom", "Canada", "France", "Spain", "Australia", "Japan"]

select_country = gapminder[gapminder["country"].isin(country_list)]

fig = px.line(data_frame = select_country, x = "year", y = "gdpPercap", color = "country", color_discrete_sequence=px.colors.colorbrewer.Set2)
fig.update_layout(title = "GDP Per Capita", xaxis_title = "Year", 
                 yaxis_title = "GDP Per Capita")
fig.show("notebook")

United States has highest GDP Per Capita from 1952 to 2007 following by Canada. GDP Per Capita is trending similar in European Countries.